1. Field of the Invention
The present invention relates to a pattern recognition system and method, which can accurately recognize an input pattern such as speech, character, figure, and the like and, more particularly, to an improvement in a partial space method.
2. Description of the Related Art
In recent years, studies about pattern recognition processing for speech, character, figure, and the like have been made, and the pattern recognition processing has been receiving a great deal of attention as an important technique for realizing a natural man-machine interface.
A pattern recognition apparatus for executing pattern recognition processing of this type basically has an arrangement shown in FIG. 1. The respective units perform the following processing.
1) A feature extracting unit 1 analyzes an input pattern to obtain its feature pattern.
2) By referring to a reference pattern memory 2 which stores reference patterns obtained in units of categories of patterns to be recognized as dictionaries, similarities or distances between the reference pattern dictionaries and the feature pattern are computed by a pattern matching unit 3.
3) The collation results are determined by a determining unit 4 to obtain a recognition result of the input pattern. Note that the determining unit 4 obtains a category name of the reference pattern which has the highest similarity value (or the smallest distance value) with the input pattern as a recognition result or a recognition candidate for the input pattern. When the input pattern is a speech waveform, the feature extracting unit 1 performs Band Pass Filter (BPF) analysis or Linear Prediction Coding (LPC) analysis of the input speech, and then detects a speech interval, thus obtaining acoustic analysis data in this speech interval as an input pattern.
When the input pattern is a character image, the feature extracting unit 1 quantizes the input character image, and then extracts a character portion, thus obtaining feature data of the character pattern as an input pattern.
As a method in the pattern recognition processing, a subspace method is known. Pattern recognition using the subspace method is described in, e.g., U.S. Pat. No. 4,624,011 (Watanabe et. al.)
In the subspace method, as reference pattern dictionaries of categories, dictionaries (.phi.(K,m); K is category name, and m is the number of an orthogonal axis; m=1, 2, . . . , M) which are orthogonalized in advance by KL expansion in units of categories are created as orthogonalized dictionary sets. Similarities S(K) between the dictionaries and an input pattern (F) are computed according to the following equation to perform pattern matching processing: ##EQU1## where (.multidot.) indicates an inner product, and .vertline..vertline. .vertline..vertline. indicates a norm.
The pattern matching method according to the subspace method is widely used in pattern recognition since it can relatively easily obtain an accurate recognition result.
In the conventional pattern recognition processing using the subspace method, as shown in the abovementioned equation, the inner products of the input pattern (F) and the orthogonal axes (.phi.(K,m)) of the orthogonalized dictionary sets are merely accumulated, and the overall feature of the input pattern is evaluated using the accumulation value. In other words, the pattern recognition is performed not by using the individual inner products obtained between the input pattern and the orthogonal axes but by using the accumulation value representing the overall feature. For this reason, when an inner product with respect to a given orthogonal axis takes a large value, which cannot be obtained with a correct pattern, due to noise, the accumulation result of the inner products tends to become a value larger than that of the inner products for the correct pattern. When pattern matching processing is performed using the subspace method of this type, a determination error (recognition error) caused by a category other than an object to be recognized and various noise components tends to occur.
In order to solve this problem, recently, pattern matching processing using a multi-layer neural network is receiving a great deal of attention. A nonlinear discriminant function is realized by the neural network, for sequentially transmitting neurons carrying information, to extract feature data of an input pattern. The neural network of this type poses the problem of how to determine a set of coefficients for defining the way of combining outputs from lower-order layers. As an algorithm for obtaining these coefficients, a back propagation (BP) algorithm is known (NATURE Vol. 323 9, pp. 553-536 (1986, Oct) Learning representations by back-propagation errors). Various reports announced that pattern matching processing could be accurately executed upon application of the BP algorithm.
However, when a neural network is used, an enormous amount of computations for determining coefficients of each layer must be performed, and a load of computation processing is very large.